[Thesis notes] energy-aware Resource Allocation Heuristics for efficient management of data centers for Clou

Timespan: 2.5-2.14
Anton beloglazov, Jemal H. abawajy, Rajkumar buyya:Energy-aware Resource Allocation Heuristics for efficient management of data centers for cloud computing. Future Generation Comp. syst. 28 (5): 755-768 (2012) (GS: 35)

The author Anton beloglazov is a PhD student at the University of Melbourne. He is also an intern at Rajkumar buyya and is interested in Distributed Systems, virtualization, and data center energy saving. The doctoral thesis entitled energy and performance efficient dynamic modeling lidation of virtual machines in cloud data centers focuses on improving the utilization of physical resources by dynamically merging virtual machines in the data center, low energy consumption is reduced when QoS constraints are met. Currently, he is still involved in openstack neat (based on the dynamic VM merge framework of openstack ).

The following is his paper:

Publication

Anton beloglazov and Rajkumar buyya, " Managing overloaded hosts for dynamic validation lidation of virtual machines in cloud data centers under quality of service constraints  " , IEEE Transactions on parallel and distributed systems (TPPS), ieee cs Press, USA, 2012  Anton beloglazov and Rajkumar buyya,  "  Openstack neat: A Framework for dynamic validation lidation of virtual machines in openstack clouds-a blueprint  " , Technical Report CLOUDS-TR- 2012 - 4 , Cloud computing and Distributed Systems Laboratory, the University of Melbourne, August 14 , 2012  Anton beloglazov, sareh fotuhi piraghaj, Hammed alrokayan, and Rajkumar buyya,  "  Deploying openstack on centos using the KVM hypervisor and glusterfs Distributed File System  " , Technical Report CLOUDS-TR- 2012 - 3 , Cloud computing and Distributed Systems Laboratory, the University of Melbourne, August 14 ,2012  Anton beloglazov and Rajkumar buyya,  "  Optimal online determin istic algorithms and adaptive Heuristics for energy and performance efficient dynamic validation lidation of virtual machines in cloud data centers  " , Concurrency and computation: practice and experience (ccpe), Volume 24 , Issue 13 , Pages: 1397 - 1420 , John Wiley & Sons, Ltd, New York, USA, 2012 Anton beloglazov, Jemal abawajy, and Rajkumar buyya,  "  Energy-aware Resource Allocation Heuristics for efficient management of data centers for cloud computing  " , The International Journal of grid computing and escience, future generation computer systems (FGCs), Volume 28 , Issue 5 , Pages: 755 - 768 , Elsevier Science, Amsterdam, The Netherlands, May 2012  Kyong Hoon Kim, Anton beloglazov and Rajkumar buyya, "  Power-aware provisioning of virtual machines for real-time cloud services  " , Concurrency and computation: practice and experience (ccpe), Volume 23 , Number 13 , Pages: 1492 - 1505 , John Wiley & Sons, Ltd, New York, USA, 2011  Anton beloglazov and Rajkumar buyya,  "  Energy-efficient internal lidation of virtual machines in cloud data centers " , Proceedings of the IBM collaborative academia research exchange workshop (I-CARE 2010 ), Bangalore, India, October 22 , 2010  Anton beloglazov and Rajkumar buyya,  "  Adaptive Threshold-based approach for energy-efficient internal lidation of virtual machines in cloud data centers  " , Proceedings of the 8th International Workshop on Middleware For Grids, clouds and E-Science (MGC 2010 ), Bangalore, India: ACM, November 29 -December 3 , 2010  Anton beloglazov, Rajkumar buyya, young Choon Lee, and Albert zomaya,  "  A Taxonomy and Survey of Energy-efficient data centers and cloud computing systems  " , Advances In Computers, Marvin v. zelkowitz (editor), Volume 82 , Pages: 47 - 111 , ISSN:0065 - 2458 , Elsevier, 2011  Rajkumar buyya, Anton beloglazov, and Jemal abawajy,  "  Energy-efficient management of data center resources for cloud computing: A vision, elastic tural elements, and open challenges  " , Proceedings of 2010 International Conference on parallel and distributed processing techniques and applications (pdpta 2010 ), Las Vegas, USA, July 12 -15 , 2010  -Keynote paperanton beloglazov and Rajkumar buyya,  "  Energy efficient allocation of virtual machines in cloud data centers  " , In Proceedings of the 10th IEEE/ACM International Symposium on Cluster, cloud and grid computing (ccgrid 2010 ), Melbourne, Australia, May 17 - 20 , 2010  Anton beloglazov and Rajkumar buyya, "  Energy efficient resource management in receivalized cloud data centers  " , Ieee tcsc doctoral symposium, in Proceedings of the 10th IEEE/ACM International Symposium on Cluster, cloud, and grid computing (ccgrid 2010 ), Melbourne, Australia, May 17 - 20 , 2010  Rodrigo N. Calheiros, Rajiv Ranjan, Anton beloglazov, Cesar A. F. de rose, and Rajkumar buyya,  " Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning Algorithms  " , Software: practice and experience (SPE), Volume 41 , Number 1 , Pages: 23 - 50 , ISSN: 0038 - 0644 , John Wiley & Sons, Ltd, New York, USA, January, 2011 . DOI: 10.1002 /SPE. 995 Anton beloglazov and Rajkumar buyya,  "  Power and performance efficient resource management in cloud computing  " , In Proceedings of the IEEE science and engineering graduate research Expo 2009 , Melbourne, Australia, 2009 , Pp. 38 - 40 

The following are the paper notes:

1. This article first proposes an energy-efficient cloud computing architecture framework and principles.

System Architecture(S3.1)

As shown in, there are four layers. Among them, green service Allocator has many roles and undertakes the functions of analyzing service user requests, SLA negotiation, service pricing, Vm scheduling, and VM management.

2. In S3.2, a power model is proposed to manage the energy consumption and CPU utilization:

Among them, Pmax is the highest energy consumption when the server is fully utilized, k is the energy proportion of idle server efficiency (usually 70%), and U is the CPU utilization.

(The energy consumption is also related to the memory, disk, and network usage, but mainly to the CPU. Therefore, the above formula only shows the CPU utilization. For details, see S3.2)

3. On this basis, (S4) proposes the resource supply and allocationAlgorithmTo improve the energy efficiency of the cloud computing environment. This algorithm is a heuristic algorithm that can improve the energy-saving performance of the data center while ensuring that the customer's QoS is satisfied.

(S4.1) "VM placement ":This is about which PM is allocated to create a new VM request to minimize the energy consumption. This problem is modeled as "bin packing problem with variable bin sizes and prices" and uses the modified "best fit decreasing (BFD)" algorithm.

(S4.2) "VM selection ":Is about how to optimize the current VM allocation to optimize energy consumption. There are two main steps: first select the migration object (a group of VMS), and then use the mbfd algorithm to determine which PMS the migration object will be placed on.

4. In view of the "when to select migration objects" (s4.2), three selection strategies are proposed (the basic idea is similar ):

• The minimization of migrations (MM) Policy
• The highest potential growth (HPG) Policy
• The random choice (RC) Policy

Take the MM policy as an example. It is the rule of this policy:

The main idea is as follows:

If the CPU usage of a PM exceeds the upper limit, find a group of VMS with the least number. This group of VMS is the migration object;

If the CPU usage of a PM is too low to reach the lower limit, all VMS on the PM will be migrated.

5. (S5) has been verified through experiments.

(S5.1) describes performance metrics:

• Total energy consumption
• SLA Violation percentage
• Number of VM migrations initiated by the VM Manager
• Average SLA Violation

The experiment was conducted on the simulation platform (cloudsim Toolkit. For detailed experiment process and results, see the original document.

6. The author puts forward related open challenges in (s6)

• Optimization of VM placement according to the utilization of multiple system resources
• Optimization of virtual network topologies
• Optimization of thermal states and cooling system operation
• efficient validation lidation of VMS for management heterogeneous workloads
• A holistic approach to energy-aware resource management